A Self-protecting Approach for Service-oriented Mobile Applications

Abstract

The evolution of software systems in the last 10 years has brought new challenges for the development area, especially for service-oriented Mobile Applications (MobApps). In the mobile computing domain, the integration of MobApps into service-based systems has been a feasible alternative to boost the capacity of processing and storage of such applications. In parallel, this type of application needs monitoring approaches mainly due to the need of dealing with a large number of users, continuous changes in the execution environment, and security threats. Besides that, most MobApps do not present the self-protecting property by default, resulting in a number of adverse situations, such as integrity of execution, reliability, security, and adaptations at runtime. The principal contribution of this paper is an approach based on MAPE-K (Monitor-Analyze-Plan-Execute over Knowledge) loop and machine learning techniques to ensure self-protecting features in MobApps, in particular, those based on services. Experimental results showed that this approach can autonomously and dynamically mitigate threats, making these applications more trustworthy and intrusion-safe. Our approach has good potential to contribute to the development of MobApps, going beyond existing approaches.